It was with a certain sense of déjà vu, combined with not inconsiderable existential boredom, that I read headlines on Sunday declaring that a group of Democratic lawmakers had fled Texas. Naturally, the media framed it as high drama, and certainly the Republicans helped with their threats to arrest their opponents gone AWOL. But I suspected high jinks.

After all, the Democrats have done this before, and not that long ago. In 2021, for instance, they attempted to derail proposed legislation limiting early voting and banning drive-through voting by skedaddling to Washington DC. Democrats, being in the minority, can’t outvote Republicans. But as the rules require two-thirds of the 150-member Texas house to be present for a vote to take place, they can just… not show up.

There’s only one problem: you can’t stay in Washington DC forever. After the requisite shenanigans and posturing in 2021, the Democrats meekly returned home, cast their votes, and lost, as was fated to happen. This was a repeat of a similar performance Texas Democrats had put on in 2003, when a group of them fled the state to avoid a vote on redistricting that was predicted to cost them seats. That time, they hung out in Oklahoma before surrendering to the inevitable.

Once again, the reason for this latest exodus was redistricting. Governor Greg Abbott had called a special legislative session, and a vote was due on a redrawn electoral map which is set to create an additional five Republican seats at next year’s midterms. The legislature is also supposed to be voting on disaster relief to support the victims of last month’s catastrophic floods, but it seems that they will have to wait until the Democrats see fit to return home.

One entertaining detail is that the majority of the errant lawmakers are taking refuge in Illinois, where the most recent congressional redistricting left Democrats with 14 out of 17 Congressional seats. So egregious is the Illinois map that the Princeton Gerrymandering Project gave it an “F” grade; even the late Hulk Hogan would have blushed to take part in such obvious kayfabe.

Redrawing maps to grant your side an unfair political advantage is a hallowed national tradition — the term “gerrymander” was coined when the US was the same age as Taylor Swift is today. It is thus hardly surprising that prominent Democratic governors such as Gavin Newsom and Kathy Hochul have suggested they should respond with some aggressive redistricting of their own. But this is easier said than done. In some states politicians do not control redistricting, and in the blue states where they do — such as New Mexico and Nevada — they have already “optimised” for Democratic votes.

And so the Texas lawmakers will enjoy their out-of-state escapade, give interviews to sympathetic journalists, post clips to social media, and do a little fundraising. Then they will return, cast their votes, lose, and Trump will get his five extra seats. And then we can all look forward to the next piece of theatre.

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Daniel Kalder is an author based in Texas. Previously, he spent ten years living in the former Soviet bloc. His latest book, Dictator Literature, is published by Oneworld. He also writes on Substack: Thus Spake Daniel Kalder.

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China is trying to catch up with America on artificial intelligence. The Wall Street Journal has reported that Zhipu AI — one of China’s six “AI tiger” LLMs — has released a new model that can rival leading US systems, including Anthropic’s Mythos, in cybersecurity tasks such as pinpointing security bugs. While this marks a milestone in China’s drive to catch up with Western AI capabilities, strong performance on a single benchmark does not mean it has taken the lead. Chinese models still lag behind their Western counterparts in broader capabilities, such as autonomous operation. Skepticism is therefore warranted before resorting to hysterical conclusions, but complacency about the geopolitical implications of China’s AI advances would be an even greater mistake.

On the infrastructure side, Chinese AI is still constrained by access to advanced chips, with American labs way ahead in computing capacity as well as investment. Analysis from earlier this year suggests that Chinese models are likely to be at least a few months behind those in the US. But they are still continuing to make progress, or that the geopolitical importance of AI will be decided only by whose LLM has ventured deeper into the technological frontier. The practical applications of AI, countries’ to capture foreign markets, and the application of AI into the real economy will matter just as much.

Here, China may hold an advantage. As with its dominance across many critical supply chains, Beijing may not need to produce the most advanced AI systems — only those that are affordable and widely deployable. In doing so, it could consolidate global influence by supplying functional, low-cost AI at scale.

Beijing seems to be pursuing exactly that path, developing an AI “open-source” strategy that offers affordable, widely available AI models for companies and individuals to use and modify as they wish. The production of the DeepSeek AI model, which matched the performance of Silicon Valley tools such as ChatGPT at a fraction of the cost for users, created goodwill among Chinese models with developers.

The four most popular models on OpenRouter, an AI hardware platform for developers, are now all Chinese. The goal for China is not only to win the frontier-model race, but to make its systems the default layer of AI adoption across industries and global markets. For most economies, the choice is increasingly between an affordable tool they can deploy now and a more robust one that may be out of reach.

And while the countries adopting Chinese models may be exposed to political pressure and cyber threats from Beijing, safer and more capable alternatives matter little if they are unaffordable. American AI companies are already under pressure to monetize products whose operating costs are rising. If Chinese open-source models become the cheap default for startups, universities, governments and businesses across the developing world, then America’s AI lead will be eroded from below.

Perhaps more concerning for America in the long run is how AI can give Chinese manufacturing even more strength, through the ongoing integration of AI as a general-purpose technology. China’s new Five-Year Plan mentioned AI more than 50 times and includes an “AI+” action plan aimed at spreading AI across the economy.

Beijing has been pioneering automation of its critical infrastructure for years, with promising recent results in increasing warplane production capacity. In that regard, China’s open-model strategy and manufacturing dominance will reinforce each other. Cheap, adaptable models accelerate deployment across the real economy while those deployments generate real-world data and use cases that can feed back into further model improvement.

The United States should not dismiss the importance of its lead in the AI race. That lead worries Beijing, not least because a more automated Chinese economy would also become more vulnerable to AI-generated cyber threats. But nor should Washington assume that China cannot catch up with American capabilities over time.

This AI competition represents part of a broader struggle over tech supply chains and geopolitical influence. Decisions over whether to adopt US or Chinese models could produce a more fragmented global reality, with different regions relying on different cloud providers, chips and security structures. The result will likely be a global economy which is divided into competing spheres, rather than one which produces a single winner.

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Miquel Vila is a political and geopolitical risk consultant focusing on industrial strategy, critical infrastructure and global supply chains.

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